Feynman is an open-source AI research agent that helps users investigate topics with language models. It supports local model providers and hosted model authentication through its setup process. The catalogue contains skills, agents, and instructions that extend Feynman’s workflows.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add companion-inc/feynman --skill runpod-computegit clone --depth 1 https://github.com/companion-inc/feynmanWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/companion-inc/feynman/runpod-compute)<a href="https://agentmods.dev/skills/companion-inc/feynman/runpod-compute"><img src="https://agentmods.dev/badge/skills/companion-inc/feynman/runpod-compute/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/companion-inc/feynman/runpod-compute"><img src="https://agentmods.dev/badge/skills/companion-inc/feynman/runpod-compute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 33 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00481 |
| Opus 5 | $0.00022 | $0.00241 |
| Sonnet 5 | $0.00009 | $0.00096 |
| Haiku 4.5 | $0.00004 | $0.00048 |
Grade C, and why
runpod-compute scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519 What it actually says
RunPod Compute
Use runpodctl CLI for persistent GPU pods with SSH access during a specific research run. Do not use this skill for provider administration outside that run; tie every pod to a replication, benchmark, or dataset-heavy research objective.
Setup
brew install runpod/runpodctl/runpodctl # macOS
runpodctl config --apiKey=YOUR_KEY
Commands
| Command | Description |
|---|---|
runpodctl create pod --gpuType "NVIDIA A100 80GB PCIe" --imageName "runpod/pytorch:2.4.0-py3.11-cuda12.4.1-devel-ubuntu22.04" --name experiment |
Create a pod |
runpodctl get pod |
List all pods |
runpodctl stop pod <id> |
Stop (preserves volume) |
runpodctl start pod <id> |
Resume a stopped pod |
runpodctl remove pod <id> |
Terminate and delete |
runpodctl gpu list |
List available GPU types and prices |
runpodctl send <file> |
Transfer files to/from pods |
runpodctl receive <code> |
Receive transferred files |
SSH access
ssh root@<IP> -p <PORT> -i ~/.ssh/id_ed25519
Get connection details from runpodctl get pod <id>. Pods must expose port 22/tcp.
GPU types
NVIDIA GeForce RTX 4090, NVIDIA RTX A6000, NVIDIA A40, NVIDIA A100 80GB PCIe, NVIDIA H100 80GB HBM3
When to use
- Long-running research experiments needing persistent state
- Large research datasets required by a replication or benchmark
- Multi-step research work with SSH access between iterations
- Always stop or remove pods after experiments
- Check availability:
command -v runpodctl
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 49 lines · 44 tokens per session scan C bd784a05414b
runpod-compute is a skill published in the GitHub repository companion-inc/feynman (8,870 stars, last pushed 13d ago), licensed MIT. It adds 44 tokens to every session and 481 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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